Building Interpretable Climate Emulators for Economics
This work addresses the need for better climate emulators in economic models of climate change, but it is incremental as it builds on existing methods like DICE-2016.
The paper tackles the problem of creating efficient and interpretable climate emulators for economic models by proposing a framework for carbon-cycle emulators and pattern-scaling techniques, showing that incorporating land-use change alters atmospheric carbon stocks and temperature trajectories, with the four-box version with land-use change substantially affecting optimal mitigation paths.
We introduce a framework for developing efficient and interpretable climate emulators (CEs) for economic models of climate change. The paper makes two main contributions. First, we propose a general framework for constructing carbon-cycle emulators (CCEs) for macroeconomic models. The framework is implemented as a generalized linear multi-reservoir (box) model that conserves key physical quantities and can be customized for specific applications. We consider three versions of the CCE, which we evaluate within a simple representative agent economic model: (i) a three-box setting comparable to DICE-2016, (ii) a four-box extension, and (iii) a four-box version that explicitly captures land-use change. While the three-box model reproduces benchmark results well and the fourth reservoir adds little, incorporating the impact of land-use change on the carbon storage capacity of the terrestrial biosphere substantially alters atmospheric carbon stocks, temperature trajectories, and the optimal mitigation path. Second, we investigate pattern-scaling techniques that transform global-mean temperature projections from CEs into spatially heterogeneous warming fields. We show how regional baseline climates, non-uniform warming, and the associated uncertainties propagate into economic damages.